ABSTRACT
Association rule mining aims to explore large transaction databases for association rules,
which may reveal the implicit relationships among the data attributes. It has turned into a
thriving research topic in data mining and has numerous practical applications, including cross
marketing, classification, text mining, Web log analysis, and recommendation systems.
The classical model of association rule mining employs the support measure, which treats every
transaction equally. In contrast, different transactions have different weights in Real-life data
sets. For example, in the market basket data, each transaction is recorded with some profit. Much
effort has been dedicated to association rule mining with reassigned weights. However, most
data types do not come with such preassigned weights, such as Web site click-stream data. There
should be some notion of importance in those data. For instance, transactions with a large
amount of items should be considered more important than transactions with only one item.
Current methods, though, are not able to estimate this type of importance and adjust the mining
results by emphasizing the important transactions.
In this paper, we introduce w-support, a new measure of item sets in databases with only binary
attributes. The basic idea behind w-support is that a frequent item set may not be as important as
it appears, because the weights of transactions are different. These weights are completely
derived from the internal structure of the database based on the assumption that good
transactions consist of good items.
This assumption is exploited by extending Kleinberg’s HITS model and algorithm to bipartite
graphs. Therefore, support is distinct from weighted support in weighted association rule mining
(WARM), where item weights are assigned. Furthermore, a new measurement framework of
association rules based on w-support is proposed. Experimental results show that w-support can
be worked out without much overhead, and interesting patterns may be discovered through this
new measurement. The rest of this paper is organized as follows: First, WARM is discussed.
Next, we present the evaluation of transactions with HITS, followed by the definition of wsupport and the corresponding mining algorithm. An interesting real-life example and
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experimental results on different types of data are given. Concluding remarks are made in the last
section.
.
Association rule mining is a key issue in data mining.
However, the classical models ignore the difference between the transactions, and the
weighted association rule mining does not work on databases with only binary attributes.
In this paper, we introduce a new measure w-support, which does not require preassigned weights.
It takes the quality of transactions into consideration using link-based models.
A fast mining algorithm is given, and a large amount of experimental results are
presented.
The weights are completely derived from the internal structure of the database based on
the
Assumption that good transactions consist of good items.
EXISTING SYSTEM:
In this system normally users can rate the products in the website .here the top ten products are
getting by using pre assigned weights, so apart from top ten products we can display the top one
product according to the support system.
Demerits of existing system:
 There is no calculation of quality transaction.
 There no estimation of awareness of user.
 Chance of getting quality product is very less.
Contact: 040-23344332, 8008491861
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PROPOSED SYSTEM:
In this system once a registered user rate a product, analysis is made on the database and the role
of the user in the rating system is identified and rate value of the product is concluded depending
upon the his role, rate value is taken from the threshold value which we get from analysis
conclusion, while the top product is to be calculated a scan is made on the database top ten user
are identified then the top product rated by them is found and saved to location, this process is
carried out for each and everyone in the top ten user list, finally a analysis is made over top
product rated by the top ten user and we get to conclusion which is the best product, by this we
get perfect result from this analysis.
Merits of proposed system:
 It provides quality of transactions for the particular user.
 It provides w-support value based on the transactions.
 By using this system easily we can study the awareness of the different users.
SYSTEM REQUIREMENT SPECIFICATION:
Hardware Requirements:
Processor
:
Pentium III / IV
Hard Disk
:
40 GB
Ram
:
256 MB
Monitor
:
15VGA Color
Mouse
:
Ball / Optical
Keyboard
:
102 Keys
Contact: 040-23344332, 8008491861
Email id: info@projectgenie.in, www.projectgenie.in
Software Requirements:
Operating System
:
Windows XP professional
Front End
:
Microsoft Visual Studio .Net 2005
Technology
:
ASP.net
Language
:
Visual C#.Net
Back End
:
SQL Server 2000
Contact: 040-23344332, 8008491861
Email id: info@projectgenie.in, www.projectgenie.in